Overview

Dataset statistics

Number of variables26
Number of observations854
Missing cells854
Missing cells (%)3.8%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory199.1 KiB
Average record size in memory238.7 B

Variable types

Numeric15
Categorical8
DateTime1
Text1
Unsupported1

Alerts

statusId has constant value "1"Constant
status has constant value "Finished"Constant
constructor_ranking_points_before_race_mean has 854 (100.0%) missing valuesMissing
resultId has unique valuesUnique
milliseconds has unique valuesUnique
constructor_ranking_points_before_race_mean is an unsupported type, check if it needs cleaning or further analysisUnsupported
points has 201 (23.5%) zerosZeros
driver_ranking_points_before_race has 73 (8.5%) zerosZeros
constructor_ranking_points_before_race has 44 (5.2%) zerosZeros

Reproduction

Analysis started2024-05-18 11:04:03.096797
Analysis finished2024-05-18 11:08:10.285789
Duration4 minutes and 7.19 seconds
Software versionydata-profiling v4.8.3
Download configurationconfig.json

Variables

resultId
Real number (ℝ)

UNIQUE 

Distinct854
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean25727.762
Minimum24966
Maximum26403
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size45.6 KiB
2024-05-18T13:08:10.509785image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/

Quantile statistics

Minimum24966
5-th percentile25047.65
Q125407.25
median25754.5
Q326078.75
95-th percentile26346.35
Maximum26403
Range1437
Interquartile range (IQR)671.5

Descriptive statistics

Standard deviation409.93776
Coefficient of variation (CV)0.015933673
Kurtosis-1.1078559
Mean25727.762
Median Absolute Deviation (MAD)337
Skewness-0.15487779
Sum21971509
Variance168048.97
MonotonicityNot monotonic
2024-05-18T13:08:10.810813image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
24966 1
 
0.1%
25951 1
 
0.1%
25953 1
 
0.1%
25966 1
 
0.1%
25967 1
 
0.1%
25968 1
 
0.1%
25969 1
 
0.1%
25970 1
 
0.1%
25971 1
 
0.1%
25972 1
 
0.1%
Other values (844) 844
98.8%
ValueCountFrequency (%)
24966 1
0.1%
24967 1
0.1%
24968 1
0.1%
24969 1
0.1%
24971 1
0.1%
24972 1
0.1%
24973 1
0.1%
24974 1
0.1%
24975 1
0.1%
24976 1
0.1%
ValueCountFrequency (%)
26403 1
0.1%
26402 1
0.1%
26401 1
0.1%
26400 1
0.1%
26399 1
0.1%
26398 1
0.1%
26397 1
0.1%
26396 1
0.1%
26395 1
0.1%
26394 1
0.1%

raceId
Real number (ℝ)

Distinct72
Distinct (%)8.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1090.9789
Minimum1051
Maximum1126
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size45.6 KiB
2024-05-18T13:08:11.104657image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/

Quantile statistics

Minimum1051
5-th percentile1055
Q11074
median1092
Q31110
95-th percentile1124
Maximum1126
Range75
Interquartile range (IQR)36

Descriptive statistics

Standard deviation21.868884
Coefficient of variation (CV)0.020045194
Kurtosis-1.1648491
Mean1090.9789
Median Absolute Deviation (MAD)18
Skewness-0.12965506
Sum931696
Variance478.24809
MonotonicityNot monotonic
2024-05-18T13:08:11.405257image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1126 19
 
2.2%
1063 18
 
2.1%
1102 18
 
2.1%
1092 18
 
2.1%
1125 17
 
2.0%
1120 17
 
2.0%
1119 17
 
2.0%
1110 17
 
2.0%
1108 17
 
2.0%
1099 17
 
2.0%
Other values (62) 679
79.5%
ValueCountFrequency (%)
1051 6
 
0.7%
1052 10
1.2%
1053 13
1.5%
1054 11
1.3%
1055 7
0.8%
1056 7
0.8%
1057 16
1.9%
1058 4
 
0.5%
1059 11
1.3%
1060 9
1.1%
ValueCountFrequency (%)
1126 19
2.2%
1125 17
2.0%
1124 9
1.1%
1123 9
1.1%
1122 11
1.3%
1121 10
1.2%
1120 17
2.0%
1119 17
2.0%
1118 9
1.1%
1117 15
1.8%

race_name
Categorical

Distinct29
Distinct (%)3.4%
Missing0
Missing (%)0.0%
Memory size45.6 KiB
Saudi Arabian Grand Prix
 
52
Miami Grand Prix
 
51
Belgian Grand Prix
 
48
Bahrain Grand Prix
 
47
British Grand Prix
 
42
Other values (24)
614 

Length

Max length25
Median length22
Mean length19.322014
Min length16

Characters and Unicode

Total characters16501
Distinct characters43
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowBahrain Grand Prix
2nd rowBahrain Grand Prix
3rd rowBahrain Grand Prix
4th rowBahrain Grand Prix
5th rowBahrain Grand Prix

Common Values

ValueCountFrequency (%)
Saudi Arabian Grand Prix 52
 
6.1%
Miami Grand Prix 51
 
6.0%
Belgian Grand Prix 48
 
5.6%
Bahrain Grand Prix 47
 
5.5%
British Grand Prix 42
 
4.9%
Italian Grand Prix 42
 
4.9%
Azerbaijan Grand Prix 41
 
4.8%
Abu Dhabi Grand Prix 39
 
4.6%
Japanese Grand Prix 37
 
4.3%
Dutch Grand Prix 35
 
4.1%
Other values (19) 420
49.2%

Length

2024-05-18T13:08:11.690600image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
prix 854
30.7%
grand 854
30.7%
saudi 52
 
1.9%
arabian 52
 
1.9%
miami 51
 
1.8%
belgian 48
 
1.7%
bahrain 47
 
1.7%
british 42
 
1.5%
italian 42
 
1.5%
azerbaijan 41
 
1.5%
Other values (28) 700
25.2%

Most occurring characters

ValueCountFrequency (%)
r 2065
12.5%
a 2010
12.2%
1929
11.7%
i 1697
10.3%
n 1485
9.0%
d 971
 
5.9%
P 896
 
5.4%
x 880
 
5.3%
G 854
 
5.2%
e 382
 
2.3%
Other values (33) 3332
20.2%

Most occurring categories

ValueCountFrequency (%)
(unknown) 16501
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
r 2065
12.5%
a 2010
12.2%
1929
11.7%
i 1697
10.3%
n 1485
9.0%
d 971
 
5.9%
P 896
 
5.4%
x 880
 
5.3%
G 854
 
5.2%
e 382
 
2.3%
Other values (33) 3332
20.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 16501
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
r 2065
12.5%
a 2010
12.2%
1929
11.7%
i 1697
10.3%
n 1485
9.0%
d 971
 
5.9%
P 896
 
5.4%
x 880
 
5.3%
G 854
 
5.2%
e 382
 
2.3%
Other values (33) 3332
20.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 16501
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
r 2065
12.5%
a 2010
12.2%
1929
11.7%
i 1697
10.3%
n 1485
9.0%
d 971
 
5.9%
P 896
 
5.4%
x 880
 
5.3%
G 854
 
5.2%
e 382
 
2.3%
Other values (33) 3332
20.2%

year
Categorical

Distinct4
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Memory size45.6 KiB
2023
294 
2022
273 
2021
212 
2024
75 

Length

Max length4
Median length4
Mean length4
Min length4

Characters and Unicode

Total characters3416
Distinct characters5
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2021
2nd row2021
3rd row2021
4th row2021
5th row2021

Common Values

ValueCountFrequency (%)
2023 294
34.4%
2022 273
32.0%
2021 212
24.8%
2024 75
 
8.8%

Length

2024-05-18T13:08:11.914785image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-18T13:08:12.138150image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
ValueCountFrequency (%)
2023 294
34.4%
2022 273
32.0%
2021 212
24.8%
2024 75
 
8.8%

Most occurring characters

ValueCountFrequency (%)
2 1981
58.0%
0 854
25.0%
3 294
 
8.6%
1 212
 
6.2%
4 75
 
2.2%

Most occurring categories

ValueCountFrequency (%)
(unknown) 3416
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
2 1981
58.0%
0 854
25.0%
3 294
 
8.6%
1 212
 
6.2%
4 75
 
2.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 3416
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
2 1981
58.0%
0 854
25.0%
3 294
 
8.6%
1 212
 
6.2%
4 75
 
2.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 3416
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
2 1981
58.0%
0 854
25.0%
3 294
 
8.6%
1 212
 
6.2%
4 75
 
2.2%
Distinct72
Distinct (%)8.4%
Missing0
Missing (%)0.0%
Memory size45.6 KiB
Minimum2021-03-28 00:00:00
Maximum2024-05-05 00:00:00
2024-05-18T13:08:12.411722image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-05-18T13:08:12.695845image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

driverId
Real number (ℝ)

Distinct29
Distinct (%)3.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean703.73302
Minimum1
Maximum859
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size45.6 KiB
2024-05-18T13:08:12.936697image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q1817
median839
Q3846
95-th percentile855
Maximum859
Range858
Interquartile range (IQR)29

Descriptive statistics

Standard deviation305.87283
Coefficient of variation (CV)0.43464328
Kurtosis1.4298636
Mean703.73302
Median Absolute Deviation (MAD)9
Skewness-1.8479687
Sum600988
Variance93558.187
MonotonicityNot monotonic
2024-05-18T13:08:13.161352image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram with fixed size bins (bins=29)
ValueCountFrequency (%)
830 66
 
7.7%
1 65
 
7.6%
832 59
 
6.9%
844 58
 
6.8%
846 57
 
6.7%
815 57
 
6.7%
4 48
 
5.6%
847 47
 
5.5%
842 46
 
5.4%
840 41
 
4.8%
Other values (19) 310
36.3%
ValueCountFrequency (%)
1 65
7.6%
4 48
5.6%
8 4
 
0.5%
9 1
 
0.1%
20 19
 
2.2%
807 14
 
1.6%
815 57
6.7%
817 32
3.7%
822 40
4.7%
825 18
 
2.1%
ValueCountFrequency (%)
859 2
 
0.2%
858 10
 
1.2%
857 20
2.3%
856 3
 
0.4%
855 22
2.6%
854 12
 
1.4%
853 2
 
0.2%
852 30
3.5%
849 12
 
1.4%
848 27
3.2%

forename
Categorical

Distinct29
Distinct (%)3.4%
Missing0
Missing (%)0.0%
Memory size45.6 KiB
Max
66 
Lewis
65 
Carlos
59 
Charles
58 
Lando
57 
Other values (24)
549 

Length

Max length9
Median length8
Mean length5.881733
Min length3

Characters and Unicode

Total characters5023
Distinct characters37
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)0.1%

Sample

1st rowLewis
2nd rowMax
3rd rowValtteri
4th rowLando
5th rowCharles

Common Values

ValueCountFrequency (%)
Max 66
 
7.7%
Lewis 65
 
7.6%
Carlos 59
 
6.9%
Charles 58
 
6.8%
Lando 57
 
6.7%
Sergio 57
 
6.7%
Fernando 48
 
5.6%
George 47
 
5.5%
Pierre 46
 
5.4%
Lance 41
 
4.8%
Other values (19) 310
36.3%

Length

2024-05-18T13:08:13.392525image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
max 66
 
7.7%
lewis 65
 
7.6%
carlos 59
 
6.9%
charles 58
 
6.8%
lando 57
 
6.7%
sergio 57
 
6.7%
fernando 48
 
5.6%
george 47
 
5.5%
pierre 46
 
5.4%
lance 41
 
4.8%
Other values (19) 310
36.3%

Most occurring characters

ValueCountFrequency (%)
e 657
13.1%
a 572
 
11.4%
r 449
 
8.9%
n 368
 
7.3%
i 363
 
7.2%
o 313
 
6.2%
s 271
 
5.4%
l 228
 
4.5%
L 175
 
3.5%
t 144
 
2.9%
Other values (27) 1483
29.5%

Most occurring categories

ValueCountFrequency (%)
(unknown) 5023
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
e 657
13.1%
a 572
 
11.4%
r 449
 
8.9%
n 368
 
7.3%
i 363
 
7.2%
o 313
 
6.2%
s 271
 
5.4%
l 228
 
4.5%
L 175
 
3.5%
t 144
 
2.9%
Other values (27) 1483
29.5%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 5023
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
e 657
13.1%
a 572
 
11.4%
r 449
 
8.9%
n 368
 
7.3%
i 363
 
7.2%
o 313
 
6.2%
s 271
 
5.4%
l 228
 
4.5%
L 175
 
3.5%
t 144
 
2.9%
Other values (27) 1483
29.5%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 5023
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
e 657
13.1%
a 572
 
11.4%
r 449
 
8.9%
n 368
 
7.3%
i 363
 
7.2%
o 313
 
6.2%
s 271
 
5.4%
l 228
 
4.5%
L 175
 
3.5%
t 144
 
2.9%
Other values (27) 1483
29.5%

surname
Categorical

Distinct29
Distinct (%)3.4%
Missing0
Missing (%)0.0%
Memory size45.6 KiB
Verstappen
66 
Hamilton
65 
Sainz
59 
Leclerc
58 
Norris
57 
Other values (24)
549 

Length

Max length10
Median length8
Mean length6.6440281
Min length4

Characters and Unicode

Total characters5674
Distinct characters43
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)0.1%

Sample

1st rowHamilton
2nd rowVerstappen
3rd rowBottas
4th rowNorris
5th rowLeclerc

Common Values

ValueCountFrequency (%)
Verstappen 66
 
7.7%
Hamilton 65
 
7.6%
Sainz 59
 
6.9%
Leclerc 58
 
6.8%
Norris 57
 
6.7%
Pérez 57
 
6.7%
Alonso 48
 
5.6%
Russell 47
 
5.5%
Gasly 46
 
5.4%
Stroll 41
 
4.8%
Other values (19) 310
36.3%

Length

2024-05-18T13:08:13.635952image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
verstappen 66
 
7.7%
hamilton 65
 
7.6%
sainz 59
 
6.9%
leclerc 58
 
6.8%
norris 57
 
6.7%
pérez 57
 
6.7%
alonso 48
 
5.6%
russell 47
 
5.5%
gasly 46
 
5.4%
stroll 41
 
4.8%
Other values (20) 313
36.5%

Most occurring characters

ValueCountFrequency (%)
e 470
 
8.3%
o 454
 
8.0%
l 453
 
8.0%
s 442
 
7.8%
a 429
 
7.6%
r 427
 
7.5%
n 409
 
7.2%
t 332
 
5.9%
i 331
 
5.8%
c 243
 
4.3%
Other values (33) 1684
29.7%

Most occurring categories

ValueCountFrequency (%)
(unknown) 5674
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
e 470
 
8.3%
o 454
 
8.0%
l 453
 
8.0%
s 442
 
7.8%
a 429
 
7.6%
r 427
 
7.5%
n 409
 
7.2%
t 332
 
5.9%
i 331
 
5.8%
c 243
 
4.3%
Other values (33) 1684
29.7%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 5674
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
e 470
 
8.3%
o 454
 
8.0%
l 453
 
8.0%
s 442
 
7.8%
a 429
 
7.6%
r 427
 
7.5%
n 409
 
7.2%
t 332
 
5.9%
i 331
 
5.8%
c 243
 
4.3%
Other values (33) 1684
29.7%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 5674
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
e 470
 
8.3%
o 454
 
8.0%
l 453
 
8.0%
s 442
 
7.8%
a 429
 
7.6%
r 427
 
7.5%
n 409
 
7.2%
t 332
 
5.9%
i 331
 
5.8%
c 243
 
4.3%
Other values (33) 1684
29.7%

constructorId
Real number (ℝ)

Distinct12
Distinct (%)1.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean84.091335
Minimum1
Maximum215
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size45.6 KiB
2024-05-18T13:08:13.832547image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q16
median51
Q3131
95-th percentile214
Maximum215
Range214
Interquartile range (IQR)125

Descriptive statistics

Standard deviation84.289548
Coefficient of variation (CV)1.0023571
Kurtosis-1.4089472
Mean84.091335
Median Absolute Deviation (MAD)50
Skewness0.43645415
Sum71814
Variance7104.7279
MonotonicityNot monotonic
2024-05-18T13:08:14.023577image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram with fixed size bins (bins=12)
ValueCountFrequency (%)
131 125
14.6%
9 123
14.4%
6 117
13.7%
1 104
12.2%
117 88
10.3%
214 81
9.5%
213 62
7.3%
3 53
6.2%
51 51
6.0%
210 44
 
5.2%
Other values (2) 6
 
0.7%
ValueCountFrequency (%)
1 104
12.2%
3 53
6.2%
6 117
13.7%
9 123
14.4%
15 3
 
0.4%
51 51
6.0%
117 88
10.3%
131 125
14.6%
210 44
 
5.2%
213 62
7.3%
ValueCountFrequency (%)
215 3
 
0.4%
214 81
9.5%
213 62
7.3%
210 44
 
5.2%
131 125
14.6%
117 88
10.3%
51 51
6.0%
15 3
 
0.4%
9 123
14.4%
6 117
13.7%

constructor_name
Categorical

Distinct12
Distinct (%)1.4%
Missing0
Missing (%)0.0%
Memory size45.6 KiB
Mercedes
125 
Red Bull
123 
Ferrari
117 
McLaren
104 
Aston Martin
88 
Other values (7)
297 

Length

Max length14
Median length12
Mean length9.1932084
Min length6

Characters and Unicode

Total characters7851
Distinct characters29
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowMercedes
2nd rowRed Bull
3rd rowMercedes
4th rowMcLaren
5th rowFerrari

Common Values

ValueCountFrequency (%)
Mercedes 125
14.6%
Red Bull 123
14.4%
Ferrari 117
13.7%
McLaren 104
12.2%
Aston Martin 88
10.3%
Alpine F1 Team 81
9.5%
AlphaTauri 62
7.3%
Williams 53
6.2%
Alfa Romeo 51
6.0%
Haas F1 Team 44
 
5.2%
Other values (2) 6
 
0.7%

Length

2024-05-18T13:08:14.263533image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
f1 128
9.3%
team 128
9.3%
mercedes 125
9.1%
red 123
9.0%
bull 123
9.0%
ferrari 117
8.5%
mclaren 104
 
7.6%
aston 88
 
6.4%
martin 88
 
6.4%
alpine 81
 
5.9%
Other values (7) 267
19.5%

Most occurring characters

ValueCountFrequency (%)
e 982
 
12.5%
a 756
 
9.6%
r 733
 
9.3%
l 546
 
7.0%
518
 
6.6%
i 454
 
5.8%
n 361
 
4.6%
M 317
 
4.0%
s 310
 
3.9%
A 282
 
3.6%
Other values (19) 2592
33.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 7851
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
e 982
 
12.5%
a 756
 
9.6%
r 733
 
9.3%
l 546
 
7.0%
518
 
6.6%
i 454
 
5.8%
n 361
 
4.6%
M 317
 
4.0%
s 310
 
3.9%
A 282
 
3.6%
Other values (19) 2592
33.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 7851
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
e 982
 
12.5%
a 756
 
9.6%
r 733
 
9.3%
l 546
 
7.0%
518
 
6.6%
i 454
 
5.8%
n 361
 
4.6%
M 317
 
4.0%
s 310
 
3.9%
A 282
 
3.6%
Other values (19) 2592
33.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 7851
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
e 982
 
12.5%
a 756
 
9.6%
r 733
 
9.3%
l 546
 
7.0%
518
 
6.6%
i 454
 
5.8%
n 361
 
4.6%
M 317
 
4.0%
s 310
 
3.9%
A 282
 
3.6%
Other values (19) 2592
33.0%
Distinct40
Distinct (%)4.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean24.435013
Minimum21.888706
Maximum26.523905
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size45.6 KiB
2024-05-18T13:08:14.518597image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/

Quantile statistics

Minimum21.888706
5-th percentile22.4348
Q123.951578
median24.331589
Q324.943854
95-th percentile26.004952
Maximum26.523905
Range4.6351989
Interquartile range (IQR)0.99227535

Descriptive statistics

Standard deviation0.87202156
Coefficient of variation (CV)0.035687379
Kurtosis0.79527207
Mean24.435013
Median Absolute Deviation (MAD)0.40311177
Skewness-0.34748187
Sum20867.501
Variance0.76042161
MonotonicityNot monotonic
2024-05-18T13:08:14.777420image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram with fixed size bins (bins=40)
ValueCountFrequency (%)
23.92847727 39
 
4.6%
24.65968421 39
 
4.6%
23.95157831 39
 
4.6%
23.8815375 38
 
4.4%
24.11813333 38
 
4.4%
23.66263768 36
 
4.2%
24.1285875 35
 
4.1%
24.33158904 35
 
4.1%
24.30185714 35
 
4.1%
23.97802564 34
 
4.0%
Other values (30) 486
56.9%
ValueCountFrequency (%)
21.88870588 11
 
1.3%
22.021375 3
 
0.4%
22.24631579 10
 
1.2%
22.32884211 11
 
1.3%
22.4348 12
 
1.4%
22.9196087 10
 
1.2%
23.66263768 36
4.2%
23.69121053 4
 
0.5%
23.8815375 38
4.4%
23.89947826 7
 
0.8%
ValueCountFrequency (%)
26.52390476 3
 
0.4%
26.12269014 5
 
0.6%
26.04976389 18
2.1%
26.00495238 18
2.1%
25.84651316 24
2.8%
25.82255814 14
1.6%
25.65984058 20
2.3%
25.4671573 22
2.6%
25.43355072 9
 
1.1%
25.24832258 20
2.3%

start_position
Real number (ℝ)

Distinct20
Distinct (%)2.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean8.5374707
Minimum1
Maximum20
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size45.6 KiB
2024-05-18T13:08:15.006905image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q14
median8
Q313
95-th percentile18
Maximum20
Range19
Interquartile range (IQR)9

Descriptive statistics

Standard deviation5.4083324
Coefficient of variation (CV)0.63348181
Kurtosis-0.93893058
Mean8.5374707
Median Absolute Deviation (MAD)4
Skewness0.41116325
Sum7291
Variance29.25006
MonotonicityNot monotonic
2024-05-18T13:08:15.246981image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram with fixed size bins (bins=20)
ValueCountFrequency (%)
2 65
 
7.6%
1 62
 
7.3%
3 62
 
7.3%
6 61
 
7.1%
4 60
 
7.0%
7 56
 
6.6%
5 55
 
6.4%
8 47
 
5.5%
9 47
 
5.5%
11 41
 
4.8%
Other values (10) 298
34.9%
ValueCountFrequency (%)
1 62
7.3%
2 65
7.6%
3 62
7.3%
4 60
7.0%
5 55
6.4%
6 61
7.1%
7 56
6.6%
8 47
5.5%
9 47
5.5%
10 39
4.6%
ValueCountFrequency (%)
20 15
 
1.8%
19 23
2.7%
18 27
3.2%
17 27
3.2%
16 30
3.5%
15 31
3.6%
14 34
4.0%
13 33
3.9%
12 39
4.6%
11 41
4.8%

end_positionText
Categorical

Distinct20
Distinct (%)2.3%
Missing0
Missing (%)0.0%
Memory size45.6 KiB
1
72 
2
72 
3
71 
4
71 
6
68 
Other values (15)
500 

Length

Max length2
Median length1
Mean length1.2915691
Min length1

Characters and Unicode

Total characters1103
Distinct characters10
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique2 ?
Unique (%)0.2%

Sample

1st row1
2nd row2
3rd row3
4th row4
5th row6

Common Values

ValueCountFrequency (%)
1 72
 
8.4%
2 72
 
8.4%
3 71
 
8.3%
4 71
 
8.3%
6 68
 
8.0%
5 67
 
7.8%
7 64
 
7.5%
8 63
 
7.4%
9 57
 
6.7%
10 48
 
5.6%
Other values (10) 201
23.5%

Length

2024-05-18T13:08:15.515326image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
1 72
 
8.4%
2 72
 
8.4%
3 71
 
8.3%
4 71
 
8.3%
6 68
 
8.0%
5 67
 
7.8%
7 64
 
7.5%
8 63
 
7.4%
9 57
 
6.7%
10 48
 
5.6%
Other values (10) 201
23.5%

Most occurring characters

ValueCountFrequency (%)
1 365
33.1%
2 111
 
10.1%
3 99
 
9.0%
4 99
 
9.0%
5 91
 
8.3%
6 86
 
7.8%
7 79
 
7.2%
8 66
 
6.0%
9 58
 
5.3%
0 49
 
4.4%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1103
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
1 365
33.1%
2 111
 
10.1%
3 99
 
9.0%
4 99
 
9.0%
5 91
 
8.3%
6 86
 
7.8%
7 79
 
7.2%
8 66
 
6.0%
9 58
 
5.3%
0 49
 
4.4%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1103
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
1 365
33.1%
2 111
 
10.1%
3 99
 
9.0%
4 99
 
9.0%
5 91
 
8.3%
6 86
 
7.8%
7 79
 
7.2%
8 66
 
6.0%
9 58
 
5.3%
0 49
 
4.4%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1103
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
1 365
33.1%
2 111
 
10.1%
3 99
 
9.0%
4 99
 
9.0%
5 91
 
8.3%
6 86
 
7.8%
7 79
 
7.2%
8 66
 
6.0%
9 58
 
5.3%
0 49
 
4.4%

end_position
Real number (ℝ)

Distinct20
Distinct (%)2.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean7.147541
Minimum1
Maximum20
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size45.6 KiB
2024-05-18T13:08:15.732868image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q13
median7
Q310
95-th percentile15
Maximum20
Range19
Interquartile range (IQR)7

Descriptive statistics

Standard deviation4.3884386
Coefficient of variation (CV)0.6139788
Kurtosis-0.64187733
Mean7.147541
Median Absolute Deviation (MAD)3
Skewness0.48016158
Sum6104
Variance19.258394
MonotonicityNot monotonic
2024-05-18T13:08:16.023546image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram with fixed size bins (bins=20)
ValueCountFrequency (%)
1 72
 
8.4%
2 72
 
8.4%
3 71
 
8.3%
4 71
 
8.3%
6 68
 
8.0%
5 67
 
7.8%
7 64
 
7.5%
8 63
 
7.4%
9 57
 
6.7%
10 48
 
5.6%
Other values (10) 201
23.5%
ValueCountFrequency (%)
1 72
8.4%
2 72
8.4%
3 71
8.3%
4 71
8.3%
5 67
7.8%
6 68
8.0%
7 64
7.5%
8 63
7.4%
9 57
6.7%
10 48
5.6%
ValueCountFrequency (%)
20 1
 
0.1%
19 1
 
0.1%
18 3
 
0.4%
17 15
 
1.8%
16 18
 
2.1%
15 24
2.8%
14 28
3.3%
13 28
3.3%
12 38
4.4%
11 45
5.3%

points
Real number (ℝ)

ZEROS 

Distinct23
Distinct (%)2.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean8.2394614
Minimum0
Maximum26
Zeros201
Zeros (%)23.5%
Negative0
Negative (%)0.0%
Memory size45.6 KiB
2024-05-18T13:08:16.295445image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11
median6
Q313
95-th percentile25
Maximum26
Range26
Interquartile range (IQR)12

Descriptive statistics

Standard deviation7.8290483
Coefficient of variation (CV)0.95018934
Kurtosis-0.45450985
Mean8.2394614
Median Absolute Deviation (MAD)6
Skewness0.74121234
Sum7036.5
Variance61.293998
MonotonicityNot monotonic
2024-05-18T13:08:16.585010image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram with fixed size bins (bins=23)
ValueCountFrequency (%)
0 201
23.5%
12 66
 
7.7%
15 65
 
7.6%
10 63
 
7.4%
8 63
 
7.4%
6 61
 
7.1%
4 61
 
7.1%
2 58
 
6.8%
18 54
 
6.3%
25 48
 
5.6%
Other values (13) 114
13.3%
ValueCountFrequency (%)
0 201
23.5%
0.5 1
 
0.1%
1 47
 
5.5%
2 58
 
6.8%
3 1
 
0.1%
4 61
 
7.1%
5 3
 
0.4%
6 61
 
7.1%
7 3
 
0.4%
7.5 1
 
0.1%
ValueCountFrequency (%)
26 23
 
2.7%
25 48
5.6%
19 17
 
2.0%
18 54
6.3%
16 5
 
0.6%
15 65
7.6%
13 4
 
0.5%
12.5 1
 
0.1%
12 66
7.7%
11 3
 
0.4%

driver_ranking_points_before_race
Real number (ℝ)

ZEROS 

Distinct247
Distinct (%)28.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean77.448478
Minimum0
Maximum575
Zeros73
Zeros (%)8.5%
Negative0
Negative (%)0.0%
Memory size42.3 KiB
2024-05-18T13:08:16.913387image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q110
median40
Q3116
95-th percentile265
Maximum575
Range575
Interquartile range (IQR)106

Descriptive statistics

Standard deviation93.265125
Coefficient of variation (CV)1.2042215
Kurtosis3.9737411
Mean77.448478
Median Absolute Deviation (MAD)36.5
Skewness1.836137
Sum66141
Variance8698.3836
MonotonicityNot monotonic
2024-05-18T13:08:17.240726image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 73
 
8.5%
4 24
 
2.8%
1 23
 
2.7%
6 21
 
2.5%
2 20
 
2.3%
12 20
 
2.3%
10 16
 
1.9%
18 13
 
1.5%
16 13
 
1.5%
15 12
 
1.4%
Other values (237) 619
72.5%
ValueCountFrequency (%)
0 73
8.5%
1 23
 
2.7%
2 20
 
2.3%
3 9
 
1.1%
4 24
 
2.8%
5 12
 
1.4%
6 21
 
2.5%
7 6
 
0.7%
8 10
 
1.2%
9 8
 
0.9%
ValueCountFrequency (%)
575 1
0.1%
549 1
0.1%
524 1
0.1%
491 1
0.1%
466 1
0.1%
454 1
0.1%
433 1
0.1%
429 1
0.1%
416 1
0.1%
400 1
0.1%
Distinct22
Distinct (%)2.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean8.6896956
Minimum1
Maximum22
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size42.3 KiB
2024-05-18T13:08:17.567912image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q14
median8
Q313
95-th percentile19
Maximum22
Range21
Interquartile range (IQR)9

Descriptive statistics

Standard deviation5.5801492
Coefficient of variation (CV)0.64215704
Kurtosis-0.87969473
Mean8.6896956
Median Absolute Deviation (MAD)4
Skewness0.44907087
Sum7421
Variance31.138065
MonotonicityNot monotonic
2024-05-18T13:08:17.836807image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram with fixed size bins (bins=22)
ValueCountFrequency (%)
1 66
 
7.7%
6 61
 
7.1%
7 61
 
7.1%
3 60
 
7.0%
2 59
 
6.9%
4 59
 
6.9%
5 55
 
6.4%
8 49
 
5.7%
9 43
 
5.0%
10 40
 
4.7%
Other values (12) 301
35.2%
ValueCountFrequency (%)
1 66
7.7%
2 59
6.9%
3 60
7.0%
4 59
6.9%
5 55
6.4%
6 61
7.1%
7 61
7.1%
8 49
5.7%
9 43
5.0%
10 40
4.7%
ValueCountFrequency (%)
22 2
 
0.2%
21 10
 
1.2%
20 13
 
1.5%
19 26
3.0%
18 26
3.0%
17 28
3.3%
16 25
2.9%
15 32
3.7%
14 35
4.1%
13 37
4.3%
Distinct256
Distinct (%)30.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean151.87705
Minimum0
Maximum860
Zeros44
Zeros (%)5.2%
Negative0
Negative (%)0.0%
Memory size42.3 KiB
2024-05-18T13:08:18.120965image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q121
median83
Q3235.75
95-th percentile522.05
Maximum860
Range860
Interquartile range (IQR)214.75

Descriptive statistics

Standard deviation173.9571
Coefficient of variation (CV)1.1453811
Kurtosis2.0061145
Mean151.87705
Median Absolute Deviation (MAD)73
Skewness1.5286161
Sum129703
Variance30261.072
MonotonicityNot monotonic
2024-05-18T13:08:18.411645image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 44
 
5.2%
34 19
 
2.2%
3 15
 
1.8%
16 15
 
1.8%
1 13
 
1.5%
2 12
 
1.4%
120 12
 
1.4%
8 12
 
1.4%
12 12
 
1.4%
11 10
 
1.2%
Other values (246) 690
80.8%
ValueCountFrequency (%)
0 44
5.2%
1 13
 
1.5%
2 12
 
1.4%
3 15
 
1.8%
4 6
 
0.7%
5 4
 
0.5%
6 8
 
0.9%
7 9
 
1.1%
8 12
 
1.4%
9 10
 
1.2%
ValueCountFrequency (%)
860 2
0.2%
822 2
0.2%
782 2
0.2%
759 2
0.2%
731 2
0.2%
719 2
0.2%
706 1
0.1%
696 2
0.2%
657 2
0.2%
656 2
0.2%
Distinct10
Distinct (%)1.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.6042155
Minimum1
Maximum10
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size42.3 KiB
2024-05-18T13:08:18.647391image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q12
median4
Q37
95-th percentile10
Maximum10
Range9
Interquartile range (IQR)5

Descriptive statistics

Standard deviation2.761786
Coefficient of variation (CV)0.59983856
Kurtosis-0.96593568
Mean4.6042155
Median Absolute Deviation (MAD)2
Skewness0.42339794
Sum3932
Variance7.6274617
MonotonicityNot monotonic
2024-05-18T13:08:18.869428image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%)
1 124
14.5%
2 119
13.9%
3 119
13.9%
4 102
11.9%
5 84
9.8%
6 76
8.9%
7 70
8.2%
9 61
7.1%
8 50
5.9%
10 49
 
5.7%
ValueCountFrequency (%)
1 124
14.5%
2 119
13.9%
3 119
13.9%
4 102
11.9%
5 84
9.8%
6 76
8.9%
7 70
8.2%
8 50
5.9%
9 61
7.1%
10 49
 
5.7%
ValueCountFrequency (%)
10 49
 
5.7%
9 61
7.1%
8 50
5.9%
7 70
8.2%
6 76
8.9%
5 84
9.8%
4 102
11.9%
3 119
13.9%
2 119
13.9%
1 124
14.5%

laps
Real number (ℝ)

Distinct19
Distinct (%)2.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean57.202576
Minimum1
Maximum78
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size45.6 KiB
2024-05-18T13:08:19.088399image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile44
Q152
median57
Q366
95-th percentile72
Maximum78
Range77
Interquartile range (IQR)14

Descriptive statistics

Standard deviation12.360355
Coefficient of variation (CV)0.21608039
Kurtosis7.2733123
Mean57.202576
Median Absolute Deviation (MAD)6
Skewness-1.9517433
Sum48851
Variance152.77838
MonotonicityNot monotonic
2024-05-18T13:08:19.329395image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram with fixed size bins (bins=19)
ValueCountFrequency (%)
57 106
12.4%
71 86
10.1%
53 82
9.6%
58 79
9.3%
50 69
 
8.1%
56 61
 
7.1%
51 57
 
6.7%
70 56
 
6.6%
52 42
 
4.9%
66 38
 
4.4%
Other values (9) 178
20.8%
ValueCountFrequency (%)
1 18
 
2.1%
28 18
 
2.1%
44 30
 
3.5%
50 69
8.1%
51 57
6.7%
52 42
 
4.9%
53 82
9.6%
56 61
7.1%
57 106
12.4%
58 79
9.3%
ValueCountFrequency (%)
78 15
 
1.8%
72 35
4.1%
71 86
10.1%
70 56
6.6%
66 38
4.4%
64 14
 
1.6%
63 22
 
2.6%
62 14
 
1.6%
59 12
 
1.4%
58 79
9.3%

time
Text

Distinct852
Distinct (%)99.8%
Missing0
Missing (%)0.0%
Memory size45.6 KiB
2024-05-18T13:08:19.741598image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/

Length

Max length11
Median length10
Mean length7.823185
Min length6

Characters and Unicode

Total characters6681
Distinct characters13
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique850 ?
Unique (%)99.5%

Sample

1st row1:32:03.897
2nd row+0.745
3rd row+37.383
4th row+46.466
5th row+59.090
ValueCountFrequency (%)
58.123 2
 
0.2%
46.358 2
 
0.2%
35.743 1
 
0.1%
1:34:31.421 1
 
0.1%
39.735 1
 
0.1%
33.530 1
 
0.1%
23.702 1
 
0.1%
37.383 1
 
0.1%
46.466 1
 
0.1%
59.090 1
 
0.1%
Other values (842) 842
98.6%
2024-05-18T13:08:20.393546image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
. 854
12.8%
1 851
12.7%
+ 782
11.7%
2 563
8.4%
3 498
7.5%
4 463
6.9%
5 442
6.6%
0 436
6.5%
: 400
 
6.0%
6 359
 
5.4%
Other values (3) 1033
15.5%

Most occurring categories

ValueCountFrequency (%)
(unknown) 6681
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
. 854
12.8%
1 851
12.7%
+ 782
11.7%
2 563
8.4%
3 498
7.5%
4 463
6.9%
5 442
6.6%
0 436
6.5%
: 400
 
6.0%
6 359
 
5.4%
Other values (3) 1033
15.5%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 6681
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
. 854
12.8%
1 851
12.7%
+ 782
11.7%
2 563
8.4%
3 498
7.5%
4 463
6.9%
5 442
6.6%
0 436
6.5%
: 400
 
6.0%
6 359
 
5.4%
Other values (3) 1033
15.5%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 6681
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
. 854
12.8%
1 851
12.7%
+ 782
11.7%
2 563
8.4%
3 498
7.5%
4 463
6.9%
5 442
6.6%
0 436
6.5%
: 400
 
6.0%
6 359
 
5.4%
Other values (3) 1033
15.5%

milliseconds
Real number (ℝ)

UNIQUE 

Distinct854
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5906715.6
Minimum207071
Maximum11012095
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size45.6 KiB
2024-05-18T13:08:20.721559image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/

Quantile statistics

Minimum207071
5-th percentile4833677.4
Q15278019
median5640322
Q36139782
95-th percentile8427837.5
Maximum11012095
Range10805024
Interquartile range (IQR)861763

Descriptive statistics

Standard deviation1470523.9
Coefficient of variation (CV)0.24895797
Kurtosis5.5602297
Mean5906715.6
Median Absolute Deviation (MAD)385976
Skewness0.049781128
Sum5.0443351 × 109
Variance2.1624406 × 1012
MonotonicityNot monotonic
2024-05-18T13:08:21.031198image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
5523897 1
 
0.1%
6593870 1
 
0.1%
6595371 1
 
0.1%
5277940 1
 
0.1%
5302030 1
 
0.1%
5310329 1
 
0.1%
5313752 1
 
0.1%
5323638 1
 
0.1%
5341260 1
 
0.1%
5342067 1
 
0.1%
Other values (844) 844
98.8%
ValueCountFrequency (%)
207071 1
0.1%
209066 1
0.1%
209672 1
0.1%
211567 1
0.1%
214550 1
0.1%
217248 1
0.1%
218650 1
0.1%
219679 1
0.1%
222556 1
0.1%
223237 1
0.1%
ValueCountFrequency (%)
11012095 1
0.1%
10996527 1
0.1%
10991047 1
0.1%
10990500 1
0.1%
10990020 1
0.1%
10979603 1
0.1%
10977908 1
0.1%
10976881 1
0.1%
10974786 1
0.1%
10974147 1
0.1%

statusId
Categorical

CONSTANT 

Distinct1
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size45.6 KiB
1
854 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters854
Distinct characters1
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row1
4th row1
5th row1

Common Values

ValueCountFrequency (%)
1 854
100.0%

Length

2024-05-18T13:08:21.288662image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-18T13:08:21.471709image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
ValueCountFrequency (%)
1 854
100.0%

Most occurring characters

ValueCountFrequency (%)
1 854
100.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 854
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
1 854
100.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 854
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
1 854
100.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 854
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
1 854
100.0%

status
Categorical

CONSTANT 

Distinct1
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size45.6 KiB
Finished
854 

Length

Max length8
Median length8
Mean length8
Min length8

Characters and Unicode

Total characters6832
Distinct characters7
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowFinished
2nd rowFinished
3rd rowFinished
4th rowFinished
5th rowFinished

Common Values

ValueCountFrequency (%)
Finished 854
100.0%

Length

2024-05-18T13:08:21.666339image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-18T13:08:21.855157image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
ValueCountFrequency (%)
finished 854
100.0%

Most occurring characters

ValueCountFrequency (%)
i 1708
25.0%
F 854
12.5%
n 854
12.5%
s 854
12.5%
h 854
12.5%
e 854
12.5%
d 854
12.5%

Most occurring categories

ValueCountFrequency (%)
(unknown) 6832
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
i 1708
25.0%
F 854
12.5%
n 854
12.5%
s 854
12.5%
h 854
12.5%
e 854
12.5%
d 854
12.5%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 6832
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
i 1708
25.0%
F 854
12.5%
n 854
12.5%
s 854
12.5%
h 854
12.5%
e 854
12.5%
d 854
12.5%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 6832
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
i 1708
25.0%
F 854
12.5%
n 854
12.5%
s 854
12.5%
h 854
12.5%
e 854
12.5%
d 854
12.5%

circuitId
Real number (ℝ)

Distinct28
Distinct (%)3.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean33.820843
Minimum1
Maximum80
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size45.6 KiB
2024-05-18T13:08:22.057299image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile3
Q19
median21.5
Q370
95-th percentile79
Maximum80
Range79
Interquartile range (IQR)61

Descriptive statistics

Standard deviation28.95389
Coefficient of variation (CV)0.85609606
Kurtosis-1.3710339
Mean33.820843
Median Absolute Deviation (MAD)15.5
Skewness0.57734982
Sum28883
Variance838.32777
MonotonicityNot monotonic
2024-05-18T13:08:22.317698image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram with fixed size bins (bins=28)
ValueCountFrequency (%)
77 52
 
6.1%
79 51
 
6.0%
13 48
 
5.6%
3 47
 
5.5%
9 42
 
4.9%
14 42
 
4.9%
73 41
 
4.8%
24 39
 
4.6%
22 37
 
4.3%
39 35
 
4.1%
Other values (18) 420
49.2%
ValueCountFrequency (%)
1 31
3.6%
3 47
5.5%
4 27
3.2%
5 9
 
1.1%
6 29
3.4%
7 31
3.6%
9 42
4.9%
11 25
2.9%
13 48
5.6%
14 42
4.9%
ValueCountFrequency (%)
80 17
 
2.0%
79 51
6.0%
78 18
 
2.1%
77 52
6.1%
75 11
 
1.3%
73 41
4.8%
71 11
 
1.3%
70 29
3.4%
69 34
4.0%
39 35
4.1%

constructor_ranking_points_before_race_mean
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing854
Missing (%)100.0%
Memory size45.6 KiB

Interactions

2024-05-18T13:07:59.761540image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-05-18T13:04:03.849377image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-05-18T13:04:12.684765image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-05-18T13:04:21.753765image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-05-18T13:04:35.972928image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-05-18T13:04:45.949504image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-05-18T13:04:58.886417image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-05-18T13:05:12.364656image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-05-18T13:05:21.774925image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-05-18T13:05:33.381819image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-05-18T13:05:43.437218image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-05-18T13:05:53.206004image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-05-18T13:06:02.757537image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-05-18T13:06:14.309493image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-05-18T13:06:23.608306image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-05-18T13:07:59.959637image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-05-18T13:04:04.070362image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-05-18T13:04:12.873666image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-05-18T13:04:22.246315image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-05-18T13:04:36.166365image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-05-18T13:04:46.248525image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-05-18T13:04:59.643064image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-05-18T13:05:12.565808image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-05-18T13:05:21.997169image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-05-18T13:05:33.631284image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-05-18T13:05:43.643708image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-05-18T13:05:53.412533image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-05-18T13:06:02.964016image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-05-18T13:06:14.523411image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-05-18T13:06:31.113521image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-05-18T13:08:00.174696image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-05-18T13:04:04.266356image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-05-18T13:04:13.064744image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-05-18T13:04:22.617222image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-05-18T13:04:36.381558image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-05-18T13:04:46.538507image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-05-18T13:05:00.296743image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-05-18T13:05:12.757716image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-05-18T13:05:22.237221image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-05-18T13:05:33.873666image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-05-18T13:05:43.850102image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-05-18T13:05:53.620872image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-05-18T13:06:03.167660image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-05-18T13:06:14.731915image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-05-18T13:06:37.084966image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-05-18T13:08:00.347594image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-05-18T13:04:04.430822image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-05-18T13:04:13.241136image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-05-18T13:04:22.977000image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-05-18T13:04:36.562561image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-05-18T13:04:46.761440image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-05-18T13:05:00.731470image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-05-18T13:05:13.075842image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-05-18T13:05:22.617719image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-05-18T13:05:34.474558image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-05-18T13:05:44.040701image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-05-18T13:05:53.801301image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-05-18T13:06:03.350638image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-05-18T13:06:14.916477image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-05-18T13:06:42.323624image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-05-18T13:08:00.534946image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-05-18T13:04:04.598757image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-05-18T13:04:13.415090image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-05-18T13:04:23.349213image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-05-18T13:04:36.743524image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-05-18T13:04:47.066235image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-05-18T13:05:01.108932image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-05-18T13:05:13.267745image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-05-18T13:05:22.944471image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-05-18T13:05:34.640479image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-05-18T13:05:44.225537image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-05-18T13:05:53.992539image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-05-18T13:06:03.533561image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-05-18T13:06:15.102476image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-05-18T13:06:47.541925image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-05-18T13:08:00.721896image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-05-18T13:04:04.773577image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-05-18T13:04:13.603433image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-05-18T13:04:23.708459image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-05-18T13:04:36.938768image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-05-18T13:04:47.385812image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-05-18T13:05:01.514606image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-05-18T13:05:13.466713image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-05-18T13:05:23.247331image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-05-18T13:05:34.875527image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-05-18T13:05:44.411235image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-05-18T13:05:54.175998image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-05-18T13:06:03.712904image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-05-18T13:06:15.302588image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-05-18T13:06:52.464416image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-05-18T13:08:00.926217image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-05-18T13:04:04.961488image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-05-18T13:04:13.808705image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-05-18T13:04:24.149894image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-05-18T13:04:37.279623image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-05-18T13:04:47.690716image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-05-18T13:05:02.110423image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-05-18T13:05:13.674717image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-05-18T13:05:23.577524image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-05-18T13:05:35.123630image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-05-18T13:05:44.613105image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-05-18T13:05:54.383351image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
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2024-05-18T13:07:00.085127image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
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2024-05-18T13:05:54.598507image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-05-18T13:06:04.473764image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-05-18T13:06:15.710253image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-05-18T13:07:07.033591image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
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2024-05-18T13:04:05.319322image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-05-18T13:04:14.240468image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-05-18T13:04:24.893396image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-05-18T13:04:37.655188image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-05-18T13:04:48.389493image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-05-18T13:05:02.845970image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-05-18T13:05:14.068599image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-05-18T13:05:24.204961image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-05-18T13:05:35.574935image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-05-18T13:05:45.171598image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
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2024-05-18T13:05:45.393305image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-05-18T13:05:55.004475image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-05-18T13:06:04.872641image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-05-18T13:06:16.100140image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-05-18T13:07:19.337230image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-05-18T13:08:01.686639image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-05-18T13:04:05.779613image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-05-18T13:04:14.627895image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-05-18T13:04:25.619377image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-05-18T13:04:38.048455image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-05-18T13:04:49.048570image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-05-18T13:05:04.162187image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-05-18T13:05:14.464792image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-05-18T13:05:24.969140image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-05-18T13:05:35.980334image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-05-18T13:05:45.594715image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-05-18T13:05:55.200521image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-05-18T13:06:05.079743image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-05-18T13:06:16.282548image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
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2024-05-18T13:06:16.966422image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-05-18T13:07:43.795027image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-05-18T13:08:09.022739image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-05-18T13:04:12.491300image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-05-18T13:04:21.307142image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-05-18T13:04:35.761413image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-05-18T13:04:45.603716image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-05-18T13:04:58.560773image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-05-18T13:05:12.164827image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-05-18T13:05:21.534782image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-05-18T13:05:33.148021image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-05-18T13:05:43.206255image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-05-18T13:05:52.998153image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-05-18T13:06:02.543356image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-05-18T13:06:14.097858image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-05-18T13:06:23.412078image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-05-18T13:07:55.947873image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/

Missing values

2024-05-18T13:08:09.368565image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
A simple visualization of nullity by column.
2024-05-18T13:08:09.993433image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

resultIdraceIdrace_nameyearrace_datedriverIdforenamesurnameconstructorIdconstructor_nameavg_season_pit_stop_durationstart_positionend_positionTextend_positionpointsdriver_ranking_points_before_racedriver_ranking_before_raceconstructor_ranking_points_before_raceconstructor_ranking_before_racelapstimemillisecondsstatusIdstatuscircuitIdconstructor_ranking_points_before_race_mean
24960249661052Bahrain Grand Prix20212021-03-281LewisHamilton131Mercedes24.11813321125.034325461561:32:03.89755238971Finished3NaN
24961249671052Bahrain Grand Prix20212021-03-28830MaxVerstappen9Red Bull23.97802612218.03511541256+0.74555246421Finished3NaN
24962249681052Bahrain Grand Prix20212021-03-28822ValtteriBottas131Mercedes24.11813333316.02033546156+37.38355612801Finished3NaN
24963249691052Bahrain Grand Prix20212021-03-28846LandoNorris1McLaren24.52700074412.01535258456+46.46655703631Finished3NaN
24965249711052Bahrain Grand Prix20212021-03-28844CharlesLeclerc6Ferrari24.3018574668.01526297356+59.09055829871Finished3NaN
24966249721052Bahrain Grand Prix20212021-03-28817DanielRicciardo1McLaren24.5270006776.01058258456+66.00455899011Finished3NaN
24967249731052Bahrain Grand Prix20212021-03-28832CarlosSainz6Ferrari24.3018578884.01457297356+67.10055909971Finished3NaN
24968249741052Bahrain Grand Prix20212021-03-28852YukiTsunoda213AlphaTauri25.65984113992.02014112656+85.69256095891Finished3NaN
24969249751052Bahrain Grand Prix20212021-03-28840LanceStroll117Aston Martin25.2061191010101.0341377756+86.71356106101Finished3NaN
24970249761052Bahrain Grand Prix20212021-03-288KimiRäikkönen51Alfa Romeo25.4335511411110.0101611956+88.86456127611Finished3NaN
resultIdraceIdrace_nameyearrace_datedriverIdforenamesurnameconstructorIdconstructor_nameavg_season_pit_stop_durationstart_positionend_positionTextend_positionpointsdriver_ranking_points_before_racedriver_ranking_before_raceconstructor_ranking_points_before_raceconstructor_ranking_before_racelapstimemillisecondsstatusIdstatuscircuitIdconstructor_ranking_points_before_race_mean
26388263941126Miami Grand Prix20242024-05-05839EstebanOcon214Alpine F1 Team24.1669551310101.00160957+39.74654896221Finished79NaN
26389263951126Miami Grand Prix20242024-05-05807NicoHülkenberg210Haas F1 Team23.899478911110.04135757+40.78954906651Finished79NaN
26390263961126Miami Grand Prix20242024-05-05842PierreGasly214Alpine F1 Team24.1669551212120.00190957+44.95854948341Finished79NaN
26391263971126Miami Grand Prix20242024-05-05857OscarPiastri1McLaren22.434800613130.038696357+49.75654996321Finished79NaN
26392263981126Miami Grand Prix20242024-05-05855GuanyuZhou15Sauber26.5239051914140.001701057+49.97954998551Finished79NaN
26393263991126Miami Grand Prix20242024-05-05817DanielRicciardo215RB F1 Team22.0213752015150.00187657+50.95655008321Finished79NaN
26394264001126Miami Grand Prix20242024-05-05822ValtteriBottas15Sauber26.5239051616160.002001057+52.35655022321Finished79NaN
26395264011126Miami Grand Prix20242024-05-05840LanceStroll117Aston Martin22.9196091117170.091040557+55.17355050491Finished79NaN
26396264021126Miami Grand Prix20242024-05-05848AlexanderAlbon3Williams23.6912111418180.00150857+1:16.09155259671Finished79NaN
26397264031126Miami Grand Prix20242024-05-05825KevinMagnussen210Haas F1 Team23.8994781819190.01145757+1:24.68355345591Finished79NaN